{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验报告：KMeans聚类算法在Iris数据集上的应用\n",
    "20221200556 杨鑫其\n",
    "## 实验目标\n",
    "- 在Iris数据集上实现基于欧氏距离的KMeans聚类算法\n",
    "- 支持自定义K值、最大迭代次数和提前停止机制\n",
    "- 可视化聚类前后的数据分布\n",
    "- 计算聚类评价指标：F-measure、ACC、NMI、RI、ARI\n",
    "## 数据集介绍\n",
    "Iris数据集是sklearn提供的经典数据集，包含150个样本，分为3个类别（Setosa、Versicolor、Virginica）。每个样本具有4个特征：萼片长度、萼片宽度、花瓣长度和花瓣宽度。数据集规模小，类别区分较为明显，适合测试聚类算法的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T06:33:00.802630Z",
     "start_time": "2025-06-13T06:33:00.787637Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, normalized_mutual_info_score, adjusted_rand_score, rand_score\n",
    "import seaborn as sns\n",
    "\n",
    "# 设置随机种子以确保结果可重复\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据加载与预处理\n",
    "加载Iris数据集并对特征进行标准化处理，以确保各特征对聚类的贡献均衡。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T06:33:00.818132Z",
     "start_time": "2025-06-13T06:33:00.805109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (150, 4)\n",
      "类别数: 3\n"
     ]
    }
   ],
   "source": [
    "# 加载Iris数据集\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 标准化数据\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 数据信息\n",
    "print(f\"数据形状: {X_scaled.shape}\")\n",
    "print(f\"类别数: {len(np.unique(y))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans算法实现\n",
    "实现基于欧氏距离的KMeans算法，支持自定义K值、最大迭代次数和提前停止机制。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T06:33:00.833091Z",
     "start_time": "2025-06-13T06:33:00.819206Z"
    }
   },
   "outputs": [],
   "source": [
    "class KMeansClustering:\n",
    "    def __init__(self, K, max_iter=100, tol=1e-4):\n",
    "        self.K = K\n",
    "        self.max_iter = max_iter\n",
    "        self.tol = tol\n",
    "        self.centroids = None\n",
    "        self.labels_ = None\n",
    "\n",
    "    def fit(self, X):\n",
    "        # 随机初始化质心\n",
    "        np.random.seed(42)\n",
    "        self.centroids = X[np.random.choice(len(X), self.K, replace=False)]\n",
    "\n",
    "        for _ in range(self.max_iter):\n",
    "            # 分配点到最近的质心\n",
    "            old_centroids = self.centroids.copy()\n",
    "            self.labels_ = self.predict(X)\n",
    "\n",
    "            # 更新质心\n",
    "            for k in range(self.K):\n",
    "                if np.sum(self.labels_ == k) > 0:\n",
    "                    self.centroids[k] = np.mean(X[self.labels_ == k], axis=0)\n",
    "\n",
    "            # 检查是否收敛\n",
    "            if np.all(np.abs(self.centroids - old_centroids) < self.tol):\n",
    "                break\n",
    "\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        # 计算点到质心的欧氏距离\n",
    "        distances = np.linalg.norm(X[:, np.newaxis] - self.centroids, axis=2)\n",
    "        return np.argmin(distances, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类与可视化\n",
    "设置K=3进行聚类，并可视化聚类前后的数据分布（使用前两个特征进行二维可视化）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T06:33:01.128942Z",
     "start_time": "2025-06-13T06:33:00.834555Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\32491\\AppData\\Local\\Temp\\ipykernel_17664\\1489254003.py:15: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 33852 (\\N{CJK UNIFIED IDEOGRAPH-843C}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29255 (\\N{CJK UNIFIED IDEOGRAPH-7247}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23485 (\\N{CJK UNIFIED IDEOGRAPH-5BBD}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32858 (\\N{CJK UNIFIED IDEOGRAPH-805A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21069 (\\N{CJK UNIFIED IDEOGRAPH-524D}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21518 (\\N{CJK UNIFIED IDEOGRAPH-540E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36136 (\\N{CJK UNIFIED IDEOGRAPH-8D28}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24515 (\\N{CJK UNIFIED IDEOGRAPH-5FC3}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 运行KMeans\n",
    "kmeans = KMeansClustering(K=3, max_iter=100, tol=1e-4)\n",
    "kmeans.fit(X_scaled)\n",
    "labels = kmeans.labels_\n",
    "\n",
    "# 可视化函数\n",
    "def plot_clusters(X, labels, centroids=None, title=''):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=50, edgecolor='k')\n",
    "    if centroids is not None:\n",
    "        plt.scatter(centroids[:, 0], centroids[:, 1], c='red', marker='*', s=200, label='质心')\n",
    "    plt.xlabel('萼片长度（标准化）')\n",
    "    plt.ylabel('萼片宽度（标准化）')\n",
    "    plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# 聚类前\n",
    "plot_clusters(X_scaled[:, :2], y, title='聚类前（真实标签）')\n",
    "\n",
    "# 聚类后\n",
    "plot_clusters(X_scaled[:, :2], labels, kmeans.centroids[:, :2], title='KMeans聚类后（K=3）')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类指标评估\n",
    "计算F-measure、ACC、NMI、RI和ARI指标。由于KMeans标签顺序随机，需对标签进行重新映射以匹配真实标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T06:33:01.300148Z",
     "start_time": "2025-06-13T06:33:01.130699Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-measure: 0.8127\n",
      "ACC: 0.8133\n",
      "NMI: 0.6427\n",
      "RI: 0.8196\n",
      "ARI: 0.5923\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32858 (\\N{CJK UNIFIED IDEOGRAPH-805A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\anaconda3\\envs\\Torch_cpu\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 标签重新映射函数\n",
    "def remap_labels(y_true, y_pred):\n",
    "    from scipy.optimize import linear_sum_assignment\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    row_ind, col_ind = linear_sum_assignment(-cm)\n",
    "    label_mapping = dict(zip(col_ind, row_ind))\n",
    "    y_pred_mapped = np.array([label_mapping[l] for l in y_pred])\n",
    "    return y_pred_mapped\n",
    "\n",
    "# 计算聚类指标\n",
    "def calculate_metrics(y_true, y_pred):\n",
    "    # 重新映射标签\n",
    "    y_pred_mapped = remap_labels(y_true, y_pred)\n",
    "    \n",
    "    # 计算混淆矩阵\n",
    "    cm = confusion_matrix(y_true, y_pred_mapped)\n",
    "    \n",
    "    # 计算F-measure（加权平均）\n",
    "    precision = np.diag(cm) / np.sum(cm, axis=0)\n",
    "    recall = np.diag(cm) / np.sum(cm, axis=1)\n",
    "    f_measure = np.nanmean(2 * precision * recall / (precision + recall))\n",
    "    \n",
    "    # 其他指标\n",
    "    acc = accuracy_score(y_true, y_pred_mapped)\n",
    "    nmi = normalized_mutual_info_score(y_true, y_pred)\n",
    "    ri = rand_score(y_true, y_pred)\n",
    "    ari = adjusted_rand_score(y_true, y_pred)\n",
    "    \n",
    "    return {\n",
    "        'F-measure': f_measure,\n",
    "        'ACC': acc,\n",
    "        'NMI': nmi,\n",
    "        'RI': ri,\n",
    "        'ARI': ari\n",
    "    }\n",
    "\n",
    "# 计算并打印指标\n",
    "metrics = calculate_metrics(y, labels)\n",
    "for metric_name, value in metrics.items():\n",
    "    print(f\"{metric_name}: {value:.4f}\")\n",
    "\n",
    "# 可视化混淆矩阵\n",
    "cm = confusion_matrix(y, remap_labels(y, labels))\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)\n",
    "plt.title('KMeans聚类混淆矩阵')\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验总结\n",
    "- 成功实现了基于欧氏距离的KMeans聚类算法，支持自定义K值、最大迭代次数和提前停止\n",
    "- 对Iris数据集进行了标准化处理并执行K=3的聚类\n",
    "- 可视化展示了聚类前后的数据分布，显示了清晰的聚类效果\n",
    "- 计算了F-measure、ACC、NMI、RI和ARI指标，通过标签重新映射确保评估准确\n",
    "## 结论\n",
    "KMeans算法在Iris数据集上表现良好，聚类结果与真实标签高度一致，指标显示了较高的聚类质量。标准化处理有助于提升聚类效果。后续可尝试不同的K值或改进初始化方法（如k-means++）以进一步优化性能。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
